PLANETALIGN是网络对齐的综合工具库，助力方法开发与评测。
PLANETALIGN: A Comprehensive Python Library for Benchmarking Network Alignment
- 整合18个数据集和14种对齐方法，支持快速开发与测试。
- 提供标准化评估流程，覆盖效果、可扩展性与鲁棒性指标。
- 适合研究网络对齐或需要基准测试的研究者使用。
网络对齐（NA）旨在识别不同网络间的节点对应关系，是多网络学习任务的关键基础。尽管NA研究日益增多，但缺乏系统化的方法开发与评测工具。本文提出PLANETALIGN，一个全面的Python网络对齐库，集成18个数据集、14种对齐方法及可扩展的API，支持便捷使用与开发。其标准化评估流程涵盖多种指标，可系统评估方法的有效性、可扩展性与鲁棒性。通过大量对比实验，揭示了现有方法的优劣。我们希望PLANETALIGN能推动对网络对齐问题的深入理解，并促进更高效、可扩展、鲁棒的算法发展。代码开源地址：https://github.com/yq-leo/PlanetAlign。
原文摘要 · Abstract (English)
Network alignment (NA) aims to identify node correspondence across different networks and serves as a critical cornerstone behind various downstream multi-network learning tasks. Despite growing research in NA, there lacks a comprehensive library that facilitates the systematic development and benchmarking of NA methods. In this work, we introduce PLANETALIGN, a comprehensive Python library for network alignment that features a rich collection of built-in datasets, methods, and evaluation pipelines with easy-to-use APIs. Specifically, PLANETALIGN integrates 18 datasets and 14 NA methods with extensible APIs for easy use and development of NA methods. Our standardized evaluation pipeline encompasses a wide range of metrics, enabling a systematic assessment of the effectiveness, scalability, and robustness of NA methods. Through extensive comparative studies, we reveal practical insights into the strengths and limitations of existing NA methods. We hope that PLANETALIGN can foster a deeper understanding of the NA problem and facilitate the development and benchmarking of more effective, scalable, and robust methods in the future. The source code of PLANETALIGN is available at https://github.com/yq-leo/PlanetAlign.
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